Research Article
Multi-diffusion Degree Centrality Measure to Maximize the Influence Spread in the Multilayer Social Networks
@INPROCEEDINGS{10.1007/978-3-319-66742-3_6, author={Ibrahima Gaye and Gervais Mendy and Samuel Ouya and Idy Diop and Diaraf Seck}, title={Multi-diffusion Degree Centrality Measure to Maximize the Influence Spread in the Multilayer Social Networks}, proceedings={e-Infrastructure and e-Services for Developing Countries. 8th International Conference, AFRICOMM 2016, Ouagadougou, Burkina Faso, December 6-7, 2016, Proceedings}, proceedings_a={AFRICOMM}, year={2017}, month={10}, keywords={Centrality measure Diffusion probability Influence maximization Mapping matrix Multilayer social network}, doi={10.1007/978-3-319-66742-3_6} }- Ibrahima Gaye
Gervais Mendy
Samuel Ouya
Idy Diop
Diaraf Seck
Year: 2017
Multi-diffusion Degree Centrality Measure to Maximize the Influence Spread in the Multilayer Social Networks
AFRICOMM
Springer
DOI: 10.1007/978-3-319-66742-3_6
Abstract
In this work, we study the influence maximization in multilayer social networks. This problem is to find a set of persons, called seeds, that maximizes the information spread in a multilayer social network. In our works, we focus in the determination of the seeds by proposing a centrality measure called - (denoted by ) based on model. We consider the persons as the most influential. This centrality measure uses firstly, the diffusion probability for each person in each layer. Secondly, it uses the contribution of the first neighbors in the diffusion process. To show the performance of our approach, we compare it with the existing heuristics like . With software and , we show that - is more performant than the benchmark heuristic.